





"""simple docstring"""





import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						lowercase   (   _UpperCAmelCase	,    unittest.TestCase   ):
      _SCREAMING_SNAKE_CASE							      = LayoutLMTokenizer
      _SCREAMING_SNAKE_CASE							      = LayoutLMTokenizerFast
      _SCREAMING_SNAKE_CASE							      = True
      _SCREAMING_SNAKE_CASE							      = True





      def        _snake_case     (			self					)						->	Optional[Any]:
            super().setUp()

            lowerCAmelCase    		=			[
                """[UNK]""",
                """[CLS]""",
                """[SEP]""",
                """want""",
                """##want""",
                """##ed""",
                """wa""",
                """un""",
                """runn""",
                """##ing""",
                """,""",
                """low""",
                """lowest""",
            ]
            lowerCAmelCase    		=			os.path.join(self.tmpdirname ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
            with open(self.vocab_file ,		"""w""" ,		encoding="""utf-8"""					) as vocab_writer:
                  vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]					)					)





      def        _snake_case     (			self ,		**lowercase					)						->	Tuple:
            return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Optional[Any]:
            lowerCAmelCase    		=			"""UNwant\u00E9d,running"""
            lowerCAmelCase    		=			"""unwanted, running"""
            return input_text, output_text





      def        _snake_case     (			self					)						->	Dict:
            lowerCAmelCase    		=			self.tokenizer_class(self.vocab_file					)

            lowerCAmelCase    		=			tokenizer.tokenize("""UNwant\u00E9d,running"""					)
            self.assertListEqual(lowercase ,		["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]					)
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase					) ,		[7, 4, 5, 10, 8, 9]					)





      def        _snake_case     (			self					)						->	int:
            pass













"""simple docstring"""





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


SCREAMING_SNAKE_CASE__   =  get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
       SCREAMING_SNAKE_CASE__   =  json.load(f)

@require_torch
class 						lowercase   (   unittest.TestCase   ):





      def        _snake_case     (			self ,		lowercase					)						->	Tuple:
            return FSMTTokenizer.from_pretrained(lowercase					)





      def        _snake_case     (			self ,		lowercase					)						->	Dict:
            lowerCAmelCase    		=			FSMTForConditionalGeneration.from_pretrained(lowercase					).to(lowercase					)
            if torch_device == "cuda":
                  model.half()
            return model





      @parameterized.expand(
          [
              ["""en-ru""", 26.0],
              ["""ru-en""", 22.0],
              ["""en-de""", 22.0],
              ["""de-en""", 29.0],
          ]					)
      @slow
      def        _snake_case     (			self ,		lowercase ,		lowercase					)						->	Dict:
            # note: this test is not testing the best performance since it only evals a small batch
            # but it should be enough to detect a regression in the output quality
            lowerCAmelCase    		=			f'facebook/wmt19-{pair}'
            lowerCAmelCase    		=			self.get_tokenizer(lowercase					)
            lowerCAmelCase    		=			self.get_model(lowercase					)

            lowerCAmelCase    		=			bleu_data[pair]["""src"""]
            lowerCAmelCase    		=			bleu_data[pair]["""tgt"""]

            lowerCAmelCase    		=			tokenizer(lowercase ,		return_tensors="""pt""" ,		truncation=lowercase ,		padding="""longest"""					).to(lowercase					)
            lowerCAmelCase    		=			model.generate(
                input_ids=batch.input_ids ,		num_beams=8 ,		)
            lowerCAmelCase    		=			tokenizer.batch_decode(
                lowercase ,		skip_special_tokens=lowercase ,		clean_up_tokenization_spaces=lowercase					)
            lowerCAmelCase    		=			calculate_bleu(lowercase ,		lowercase					)
            print(lowercase					)
            self.assertGreaterEqual(scores["""bleu"""] ,		lowercase					)






